Deep Learning Case Study for Automatic Bird Identification

Juha Niemi, Juha Tanttu

    Research output: Contribution to journalArticleScientificpeer-review

    7 Citations (Scopus)

    Abstract

    An automatic bird identification system is required for offshore wind farms in Finland. Indubitably, a radar is the obvious choice to detect flying birds, but external information is required for actual identification. We applied visual camera images as external data. The proposed system for automatic bird identification consists of a radar, a motorized video head and a single-lens reflex camera with a telephoto lens. A convolutional neural network trained with a deep learning algorithm is applied to the image classification. We also propose a data augmentation method in which images are rotated and converted in accordance with the desired color temperatures. The final identification is based on a fusion of parameters provided by the radar and the predictions of the image classifier. The sensitivity of this proposed system, on a dataset containing 9312 manually taken original images resulting in 2.44 × 106 augmented data set, is 0.9463 as an image classifier. The area under receiver operating characteristic curve for two key bird species is 0.9993 (the White-tailed Eagle) and 0.9496 (The Lesser Black-backed Gull), respectively. We proposed a novel system for automatic bird identification as a real world application. We demonstrated that our data augmentation method is suitable for image classification problem and it significantly increases the performance of the classifier.
    Original languageEnglish
    Article number2089
    Number of pages15
    JournalApplied Sciences (Switzerland)
    Volume8
    Issue number11
    DOIs
    Publication statusPublished - 29 Oct 2018
    Publication typeA1 Journal article-refereed

    Keywords

    • Machine learning
    • Deep learning
    • Convolutional neural networks
    • Classification
    • data augmentation
    • intelligent surveillance systems

    Publication forum classification

    • Publication forum level 1

    ASJC Scopus subject areas

    • Artificial Intelligence
    • Signal Processing
    • Computer Vision and Pattern Recognition

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